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Enhanced humanoid assisted human interaction model based on linear structural modeling for knowledge representation

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Abstract

Certain rules and regulations are adapted to the linguistic production model to support the decision of human computer interaction using linguistic protection rule method. This paper proposes linear linguistic advanced structural modeling (LLASM) approach to develop weighted linguistic reasoning (WLR) algorithm for the purpose of reasoning and knowledge representation. In this article human computer interaction faces optimal flow and job Characteristics experience using individual computers in the workplace which helps to overcome the issues using LLASM in a variety of organizations with Robotic assistance for verbal and non-verbal sequence using humanoid robot. This model introduces global weight and local knowledge fuzzy rules to determine the optimal flow and job characterization problem faced in linguistic reasoning method. Here, The Weighted linguistic reasoning algorithm allows fuzzy rule-based expert systems with the help of the LLASM method to execute the intelligent and flexible knowledge reasoning model with robotic assistance. Finally, case studies are presented to show the effectiveness of the re-scheduling process which benefits the proposed method using the Linguistic reasoning model.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Ramco Institute of Technology, Rajapalayam, Tamilnadu, India

    S. Periyanayagi & A. Azhagu Jaisudhan Pazhani

  2. Department of Electronics and Communication Engineering, Government College of Engineering, Dharmapuri, Tamilnadu, India

    V. Sumathy

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  1. S. Periyanayagi
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  2. A. Azhagu Jaisudhan Pazhani
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  3. V. Sumathy
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Correspondence to S. Periyanayagi.

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Periyanayagi, S., Pazhani, A.A.J. & Sumathy, V. Enhanced humanoid assisted human interaction model based on linear structural modeling for knowledge representation. J Ambient Intell Human Comput 11, 6307–6318 (2020). https://doi.org/10.1007/s12652-020-01735-3

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  • DOI: https://doi.org/10.1007/s12652-020-01735-3

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